There has been a steady evolution in retail science towards solutions with more sensitivity to business reality. Early mathematical solutions in price optimization made retailers more revenue and profit but often generated solutions that were in conflict with business rules. For example, in most retail settings the double-size box should be more than the single-size box, but not quite twice as much. Newer price optimization techniques involve performing business-rule compliance as a post-process procedure, treating business rules as absolute constraints and resolving unavoidable conflict with rule prioritization, or leaving business-rule compliance as a user task. These techniques can produce calculations that are difficult and often not followed and can lead to sub-optimal recommended prices.